

massa <- read.csv ("massa.txt", sep = ";", dec = ",")
head(massa)


which(is.na(mass$brain))

massa <- massa[-c(which(is.na(massa$brain))),]

# 2.1
massa$logbody <- log(massa$body)
massa$logbrain <- log(massa$brain)
mod1 <- lm(massa$logbrain~massa$logbody)
b <- coefficients(mod1)[[2]]

# 2.2
sim_b <- rep(NA, 2000)

sim_b[1] <- b

# 2.3 , 2.4., 2.5.
for (i in 2:2000)
{
  sim_brain <- sample(massa$brain)
  sim_b[i] <- coefficients(lm(log(sim_brain)~massa$logbody))[[2]] 
  
} 

# 2.6
hist(sim_b)

abline(v = b, col = "red")

bicaudal <- sum(abs(sim_b)>=abs(b))
bicaudal
length(sim_b)
p.bi = bicaudal/length(sim_b)
p.bi

print("A probilidade  muito baixa (p.bi = 5e-04), logo, conclui-se que as variveis so dependentes.")

